Bevel Probabilistic Selection Task Performance
Task Information
Total Number of Trials = 104
Sweet taste and bitter taste was selected by participant to reflect reward & punishment
Learning Curves for each Shape Pair
plot1## Warning: Removed 2 rows containing missing values (geom_path).## Warning: Removed 2 rows containing missing values (geom_point).
plot2## Warning: Removed 3 rows containing missing values (geom_path).## Warning: Removed 3 rows containing missing values (geom_point).
plot3
Learning Curves Together
plot4## Warning: Removed 2 rows containing missing values (geom_path).## Warning: Removed 3 rows containing missing values (geom_path).
Begin Processing Graphs for Each Participant
## Warning: package 'ggpubr' was built under R version 3.5.2## Warning: package 'data.table' was built under R version 3.5.2Plot “Heatmaps” of Outcomes During Training
mydata$outcome0[mydata$outcome == "Miss"] <- 0## Warning: Unknown or uninitialised column: 'outcome0'.mydata$outcome0[mydata$outcome == "punish"] <- -10
mydata$outcome0[mydata$outcome == "reward"] <- 10
hmTOTAL<-ggplot(mydata,aes(as.numeric(Count), as.factor(sub_num) , fill=outcome0))+
geom_tile()+
scale_fill_gradient2(low="red", high="green", na.value="black", name="")+
theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number')+
guides(fill=guide_legend(title='Outcome'))
#geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))Plot “Heatmaps” of Outcomes During Training
-10 (red) = punishment (bitter taste)
0 (white) = missed press (no taste)
10 (green) = reward (sweet taste)
hmTOTAL
Overlay with Shape Selected & Outcome
hmTOTAL1## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have 7.
## Consider specifying shapes manually if you must have them.## Warning: Removed 482 rows containing missing values (geom_point).
Split Into Groups Based on Posttest Performance
summary(data0$sensitivity_reward)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.2917 0.4444 0.5000 0.5096 0.5789 0.7500 104data0$learn[data0$sensitivity_reward < 0.444]<- "didn't learn"
data0$learn[data0$sensitivity_reward >= 0.444 & data0$sensitivity_reward < 0.5 ]<- "maybe learn"
data0$learn[ data0$sensitivity_reward >= 0.5 & data0$sensitivity_reward < 0.57 ]<- "ok"
data0$learn[ data0$sensitivity_reward >= 0.57]<- "pretty good"
summary(as.factor(data0$learn))## didn't learn maybe learn ok pretty good NA's
## 2211 1541 2609 2643 104hmTOTALgood<-ggplot(subset(data0, learn == "pretty good"),aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
geom_tile()+
scale_fill_gradient2(low="red", high="green", na.value="black", name="") +
theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number') +
guides(fill=guide_legend(title='Outcome'))
#geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
#hmTOTALgood
hmTOTALbad<-ggplot(subset(data0, learn == "didn't learn"),aes(as.numeric(Count), as.factor(sub_num) ,fill=outcome0))+
geom_tile()+
scale_fill_gradient2(low="red", high="green", na.value="black", name="") +
theme_classic()+ xlab(label = "Trial") + ylab(label= 'Subject Number') +
guides(fill=guide_legend(title='Outcome'))
#geom_point(aes(shape=as.factor(choice), size=1, color=as.factor(choice)))
#hmTOTALbad
test1<-ggarrange(hmTOTALgood,hmTOTALbad,
labels = c("Good Posttest", "Bad Posttest"),
ncol = 1, nrow = 2)Split Into Groups Based on Posttest Performance
test1
Plot “Heatmaps” of Choices During Training
-30 (pink) = Choose F (40% correct)
-20 (mid pink) = Choose D (30% correct)
-10 (light pink) = Choose B (20% correct)
0 (white) = missed press (no choice)
10 (light blue) = Choose E (60% correct)
20 (mid blue) = Choose C (70% correct)
30 (blue) = Choose A (80% correct)
test2
Are there differences in training between posstest groups?
mytable <- xtabs(~choice+learn, data=data0)
ftable(mytable) # print table ## learn didn't learn maybe learn ok pretty good
## choice
## A 362 248 417 460
## B 331 242 420 441
## C 359 236 401 425
## D 353 240 413 377
## E 332 224 410 407
## F 358 270 384 418
## Miss 116 81 164 115summary(mytable) # chi-square test of indepedence## Call: xtabs(formula = ~choice + learn, data = data0)
## Number of cases in table: 9004
## Number of factors: 2
## Test for independence of all factors:
## Chisq = 23.464, df = 18, p-value = 0.1734mytable <- xtabs(~outcome+learn, data=data0)
ftable(mytable) # print table ## learn didn't learn maybe learn ok pretty good
## outcome
## Miss 116 81 164 115
## punish 1032 717 1218 1227
## reward 1063 743 1227 1301summary(mytable) # chi-square test of indepedence## Call: xtabs(formula = ~outcome + learn, data = data0)
## Number of cases in table: 9004
## Number of factors: 2
## Test for independence of all factors:
## Chisq = 10.656, df = 6, p-value = 0.09961mytable <- xtabs(~congruent+learn, data=data0)
ftable(mytable) # print table ## learn didn't learn maybe learn ok pretty good
## congruent
## matched 1489 997 1734 1777
## mismatched 606 463 711 751
## Miss 116 81 164 115summary(mytable) # chi-square test of indepedence## Call: xtabs(formula = ~congruent + learn, data = data0)
## Number of cases in table: 9004
## Number of factors: 2
## Test for independence of all factors:
## Chisq = 13.713, df = 6, p-value = 0.03301